Eliciting and encoding expert knowledge on variable selection into classical or Bayesian species distribution models

The quality of species distribution models (SDMs) relies to a large degree on the quality of the input data, from bioclimatic indices to environmental and habitat descriptors (Austin, 2002). Recent reviews of SDM techniques, have sought to optimize predictive performance e.g. Elith et al., 2006. In general SDMs employ one of three approaches to variable selection. The simplest approach relies on the expert to select the variables, as in environmental niche models Nix, 1986 or a generalized linear model without variable selection (Miller and Franklin, 2002). A second approach explicitly incorporates variable selection into model fitting, which allows examination of particular combinations of variables. Examples include generalized linear or additive models with variable selection (Hastie et al. 2002); or classification trees with complexity or model based pruning (Breiman et al., 1984, Zeileis, 2008). A third approach uses model averaging, to summarize the overall contribution of a variable, without considering particular combinations. Examples include neural networks, boosted or bagged regression trees and Maximum Entropy as compared in Elith et al. 2006. Typically, users of SDMs will either consider a small number of variable sets, via the first approach, or else supply all of the candidate variables (often numbering more than a hundred) to the second or third approaches. Bayesian SDMs exist, with several methods for eliciting and encoding priors on model parameters (see review in Low Choy et al. 2010). However few methods have been published for informative variable selection; one example is Bayesian trees (O’Leary 2008). Here we report an elicitation protocol that helps makes explicit a priori expert judgements on the quality of candidate variables. This protocol can be flexibly applied to any of the three approaches to variable selection, described above, Bayesian or otherwise. We demonstrate how this information can be obtained then used to guide variable selection in classical or machine learning SDMs, or to define priors within Bayesian SDMs.

DAQ00141 Recurrent Selection Program in Hexaploid Wheat

Joint project with the University of Queensland that will use the recurrent selection breeding method to produce adapted, high-yielding and disease resistant wheat parents to breeders.

Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.

Marker selection by Akaike information criterion and Bayesian information criterion

We carried out a discriminant analysis with identity by descent (IBD) at each marker as inputs, and the sib pair type (affected-affected versus affected-unaffected) as the output. Using simple logistic regression for this discriminant analysis, we illustrate the importance of comparing models with different number of parameters. Such model comparisons are best carried out using either the Akaike information criterion (AIC) or the Bayesian information criterion (BIC). When AIC (or BIC) stepwise variable selection was applied to the German Asthma data set, a group of markers were selected which provide the best fit to the data (assuming an additive effect). Interestingly, these 25-26 markers were not identical to those with the highest (in magnitude) single-locus lod scores.

Host selection and parasitism behavior of lysiphlebus testaceipes: Role of plant, aphid species and instar

The aphid parasitoid Lysiphlebus testaceipes is a potentially valuable biological control agent of Aphis gossypii a major worldwide pest of cotton. One means of increasing the abundance of a biological control agent is to provide an alternative host habitat adjacent to cropping, from which they can provide pest control services in the crop. Host selection and parasitism rate of an alternative host aphid, Aphis craccivora by L. testaceipes were studied in a series of experiments that tested its host suitability relative to A. gossypii on cotton, hibiscus and mungbean. Host acceptance, as measured by number of ovipositions was much greater in A. craccivora compared to A. gossypii, while more host aphids were accepted on mungbean than cotton. When given a choice L. testaceipes attacks more 4th instar and adult stages (63% and 70%, respectively) of both hosts than 2nd instar nymphs (47%). In a switching (host choice) experiment, L. testaceipes preferentially attacked A. craccivora on mungbean over A. gossypii on cotton. Observations of parasitoid contact with A. gossypii cornicle secretion suggest it provides a useful deterrent against parasitoid attack. From these experiments it appears L. testaceipes has a preference for A. craccivora and mungbean compared to A. gossypii and cotton, in this respect using A. craccivora and mungbean as alternative habitat may not work as the parasitoid is unlikely to switch away from its preferred host. © 2012.

Quantitative trait loci detection and benefits from marker-assisted selection in dairy cattle

Knowing the chromosomal areas or actual genes affecting the traits under selection would add more information to be used in the selection decisions which would potentially lead to higher genetic response. The first objective of this study was to map quantitative trait loci (QTL) affecting economically important traits in the Finnish Ayrshire population. The second objective was to investigate the effects of using QTL information in marker-assisted selection (MAS) on the genetic response and the linkage disequilibrium between the different parts of the genome. Whole genome scans were carried out on a grand-daughter design with 12 half-sib families and a total of 493 sons. Twelve different traits were studied: milk yield, protein yield, protein content, fat yield, fat content, somatic cell score (SCS), mastitis treatments, other veterinary treatments, days open, fertility treatments, non-return rate, and calf mortality. The average spacing of the typed markers was 20 cM with 2 to 14 markers per chromosome. Associations between markers and traits were analyzed with multiple marker regression. Significance was determined by permutation and genome-wise P-values obtained by Bonferroni correction. The benefits from MAS were investigated by simulation: a conventional progeny testing scheme was compared to a scheme where QTL information was used within families to select among full-sibs in the male path. Two QTL on different chromosomes were modelled. The effects of different starting frequencies of the favourable alleles and different size of the QTL effects were evaluated. A large number of QTL, 48 in total, were detected at 5% or higher chromosome-wise significance. QTL for milk production were found on 8 chromosomes, for SCS on 6, for mastitis treatments on 1, for other veterinary treatments on 5, for days open on 7, for fertility treatments on 7, for calf mortality on 6, and for non-return rate on 2 chromosomes. In the simulation study the total genetic response was faster with MAS than with conventional selection and the advantage of MAS persisted over the studied generations. The rate of response and the difference between the selection schemes reflected clearly the changes in allele frequencies of the favourable QTL. The disequilibrium between the polygenes and QTL was always negative and it was larger with larger QTL size. The disequilibrium between the two QTL was larger with QTL of large effect and it was somewhat larger with MAS for scenarios with starting frequencies below 0.5 for QTL of moderate size and below 0.3 for large QTL. In conclusion, several QTL affecting economically important traits of dairy cattle were detected. Further studies are needed to verify these QTL, check their presence in the present breeding population, look for pleiotropy and fine map the most interesting QTL regions. The results of the simulation studies show that using MAS together with embryo transfer to pre-select young bulls within families is a useful approach to increase the genetic merit of the AI-bulls compared to conventional selection.

Selection of Corymbia citriodora for pulp productivity

Evaluation of a series of spotted gum (Corymbia citirodora) progeny trials, established in the subtropical region of Queensland, Australia, was undertaken to provide information for the development of advanced-generation breeding populations suitable for pulp production. Measurements of growth at two ages were combined with assessments of wood density and pulp yield from a selected sample of provenances to provide comparisons between provenances, to generate genetic parameter estimates and to predict genetic gain potential. Although growth at this age was moderate relative to other eucalypts, the near-infrared predictions of average wood density of 756 kg m(-3) and pulp yield of 55% indicate the species has considerable potential as a pulpwood crop. A pulp productivity breeding objective was used to identify production populations using a range of selection trait weightings to determine potential genetic gain for pulp productivity. Genetic parameters indicated (1) levels of genetic control were moderate for all traits and higher for wood property traits, (2) genetic improvements could be achieved by selection among and within provenances with greater levels of improvement available from selection within populations, (3) genotype by environment interactions were negligible, (4) genetic correlations between traits were favourable, and (5) selection of volume production alone would maximise improvements in pulp productivity.

The moderating effects of job control and selection, optimization, and compensation strategies on the age-work ability relationship

Work ability describes employees' capability to carry out their work with respect to physical and psychological job demands. This study investigated direct and interactive effects of age, job control, and the use of successful aging strategies called selection, optimization, and compensation (SOC) in predicting work ability. We assessed SOC strategies and job control by using employee self-reports, and we measured employees' work ability using supervisor ratings. Data collected from 173 health-care employees showed that job control was positively associated with work ability. Additionally, we found a three-way interaction effect of age, job control, and use of SOC strategies on work ability. Specifically, the negative relationship between age and work ability was weakest for employees with high job control and high use of SOC strategies. These results suggest that the use of successful aging strategies and enhanced control at work are conducive to maintaining the work ability of aging employees. We discuss theoretical and practical implications regarding the beneficial role of the use of SOC strategies utilized by older employees and enhanced contextual resources at work for aging employees.

The buffering effect of selection, optimization, and compensation strategy use on the relationship between problem solving demands and occupational well-being: A daily diary study

This study investigated within-person relationships between daily problem solving demands, selection, optimization, and compensation (SOC) strategy use, job satisfaction, and fatigue at work. Based on conservation of resources theory, it was hypothesized that high SOC strategy use boosts the positive relationship between problem solving demands and job satisfaction, and buffers the positive relationship between problem solving demands and fatigue. Using a daily diary study design, data were collected from 64 administrative employees who completed a general questionnaire and two daily online questionnaires over four work days. Multilevel analyses showed that problem solving demands were positively related to fatigue, but unrelated to job satisfaction. SOC strategy use was positively related to job satisfaction, but unrelated to fatigue. A buffering effect of high SOC strategy use on the demands-fatigue relationship was found, but no booster effect on the demands-satisfaction relationship. The results suggest that high SOC strategy use is a resource that protects employees from the negative effects of high problem solving demands.

Maintaining a focus on opportunities at work: The interplay between age, job complexity, and the use of selection, optimization, and compensation strategies

The concept of focus on opportunities describes how many new goals, options, and possibilities employees believe to have in their personal future at work. This study investigated the specific and shared effects of age, job complexity, and the use of successful aging strategies called selection, optimization, and compensation (SOC) in predicting focus on opportunities. Results of data collected from 133 employees of one company (mean age = 38 years, SD = 13, range 16–65 years) showed that age was negatively, and job complexity and use of SOC strategies were positively related to focus on opportunities. In addition, older employees in high-complexity jobs and older employees in low-complexity jobs with high use of SOC strategies were better able to maintain a focus on opportunities than older employees in low-complexity jobs with low use of SOC strategies.

Scale- and Context-Dependent Selection of Recreational Harvest Estimation Methods: The Australasian Experience

Fisheries managers are becoming increasingly aware of the need to quantify all forms of harvest, including that by recreational fishers. This need has been driven by both a growing recognition of the potential impact that noncommercial fishers can have on exploited resources and the requirement to allocate catch limits between different sectors of the wider fishing community in many jurisdictions. Marine recreational fishers are rarely required to report any of their activity, and some form of survey technique is usually required to estimate levels of recreational catch and effort. In this review, we describe and discuss studies that have attempted to estimate the nature and extent of recreational harvests of marine fishes in New Zealand and Australia over the past 20 years. We compare studies by method to show how circumstances dictate their application and to highlight recent developments that other researchers may find of use. Although there has been some convergence of approach, we suggest that context is an important consideration, and many of the techniques discussed here have been adapted to suit local conditions and to address recognized sources of bias. Much of this experience, along with novel improvements to existing approaches, have been reported only in "gray" literature because of an emphasis on providing estimates for immediate management purposes. This paper brings much of that work together for the first time, and we discuss how others might benefit from our experience.

Multi-trait assessment of early-in-life female, male and genomic measures for use in genetic selection to improve female reproductive performance of Brahman cattle

Early-in-life female and male measures with potential to be practical genetic indicators were chosen from earlier analyses and examined together with genomic measures for multi-trait use to improve female reproduction of Brahman cattle. Combinations of measures were evaluated on the genetic gains expected from selection of sires and dams for each of age at puberty (AGECL, i.e. first observation of a corpus luteum), lactation anoestrous interval in 3-year-old cows (LAI), and lifetime annual weaning rate (LAWR, i.e. the weaning rate of cows based on the number of annual matings they experienced over six possible matings). Selection was on an index of comparable records for each combination. Selection intensities were less than theoretically possible but assumed a concerted selection effort was able to be made across the Brahman breed. The results suggested that substantial genetic gains could be possible but need to be confirmed in other data. The estimated increase in LAWR in 10 years, for combinations without or with genomic measures, ranged from 8 to 12 calves weaned per 100 cows from selection of sires, and from 12 to 15 calves weaned per 100 cows from selection of sires and dams. Corresponding reductions in LAI were 60-103 days or 94-136 days, and those for AGECL were 95-125 or 141-176 days, respectively. Coat score (a measure of the sleekness or wooliness of the coat) and hip height in females, and preputial eversion and liveweight in males, were measures that may warrant wider recording for Brahman female reproduction genetic evaluation. Pregnancy-test outcomes from Matings 1 and 2 also should be recorded. Percentage normal sperm may be important to record for reducing LAI and scrotal size and serum insulin-like growth factor-I concentration in heifers at 18 months for reducing AGECL. Use of a genomic estimated breeding value (EBV) in combination with other measures added to genetic gains, especially at genomic EBV accuracies of 40%. Accuracies of genomic EBVs needed to approach 60% for the genomic EBV to be the most important contributor to gains in the combinations of measures studied.

Influences on novice drivers' speed selection - a systematic review: Application and limitations of theory and methodology

Background Excessive speed is a primary contributing factor to young novice road trauma, including intentional and unintentional speeds above posted limits or too fast for conditions. The objective of this research was to conduct a systematic review of recent investigations into novice drivers’ speed selection, with particular attention to applications and limitations of theory and methodology. Method Systematic searches of peer-reviewed and grey literature were conducted during September 2014. Abstract reviews identified 71 references potentially meeting selection criteria of investigations since the year 2000 into factors that influence (directly or indirectly) actual speed (i.e., behaviour or performance) of young (age <25 years) and/or novice (recently-licensed) drivers. Results Full paper reviews resulted in 30 final references: 15 focused on intentional speeding and 15 on broader speed selection investigations. Both sets identified a range of individual (e.g., beliefs, personality) and social (e.g., peer, adult) influences, were predominantly theory-driven and applied cross-sectional designs. Intentional speed investigations largely utilised self-reports while other investigations more often included actual driving (simulated or ‘real world’). The latter also identified cognitive workload and external environment influences, as well as targeted interventions. Discussion and implications Applications of theory have shifted the novice speed-related literature beyond a simplistic focus on intentional speeding as human error. The potential to develop a ‘grand theory’ of intentional speeding emerged and to fill gaps to understand broader speed selection influences. This includes need for future investigations of vehicle-related and physical environment-related influences and methodologies that move beyond cross-sectional designs and rely less on self-reports.

Selection, optimization, and compensation strategies: Interactive effects on daily work engagement

The theory of selective optimization with compensation (SOC) proposes that the “orchestrated” use of three distinct action regulation strategies (selection, optimization, and compensation) leads to positive employee outcomes. Previous research examined overall scores and additive models (i.e., main effects) of SOC strategies instead of interaction models in which SOC strategies mutually enhance each other's effects. Thus, a central assumption of SOC theory remains untested. In addition, most research on SOC strategies has been cross-sectional, assuming that employees' use of SOC strategies is stable over time. We conducted a quantitative diary study across nine work days (N = 77; 514 daily entries) to investigate interactive effects of daily SOC strategies on daily work engagement. Results showed that optimization and compensation, but not selection, had positive main effects on work engagement. Moreover, a significant three-way interaction effect indicated that the relationship between selection and work engagement was positive only when both optimization and compensation were high, whereas the relationship was negative when optimization was low and compensation was high. We discuss implications for future research and practice regarding the use of SOC strategies at work.